Um dos modelos experimentais mais utilizados para estudar a epilepsia do lobo temporal (ELT) é o abrasamento (kindling) por estimulação elétrica diária da amígdala, o abrasamento elétrico convencional. Uma alternativa rápida e eficaz a esse modelo é o abrasamento elétrico rápido, também capaz de gerar crises límbicas, porém com 10 estímulos elétricos aplicados ao dia, por 2 dias. No 3º dia é aplicado um estímulo elétrico adicional, o 21º estímulo, quando podem ser testadas drogas antiepilépticas ou estudados mecanismos de plasticidade ou memória. Entre as principais áreas ativadas nas crises límbicas encontram-se o complexo amigdalóide, a formação hipocampal, o córtex piriforme e neocórtices adjacentes. O envolvimento de estruturas olfatórias na ELT é antigo e estudos indicam que a exposição a um estímulo olfatório é capaz de suprimir, inibir ou induzir a ocorrência de crises. Todas as evidências clínicas e experimentais dão suporte científico para a hipótese de que a estimulação olfatória com o 2,5-Dihydro-2,4,5-trimethylthiazoline (TMT), uma potente substância química, derivada das fezes de raposa e que biologicamente representa o cheiro de predador pode influenciar no processo de crises evocadas por estimulação elétrica da amígdala. O objetivo geral do presente estudo foi avaliar a influência da apresentação do estímulo olfatório com TMT nas crises epilépticas de ratos Wistar, submetidos ao abrasamento elétrico rápido da amígdala. Para tanto, os parâmetros químicos do TMT foram avaliados, bem como as respostas comportamentais de ratos Wistar machos naives submetidos ao estímulo olfatório com diferentes doses de TMT. Na sequência, um novo grupo de ratos Wistar machos naives foi submetido ao protocolo de abrasamento elétrico rápido da amígdala com a aquisição dos registros eletrencefalográficos (EEGráficos) do córtex piriforme, formação hipocampal além do complexo amigdalóide. Após abrasados os animais foram expostos ao TMT ou água destilada, previamente ao 21º estímulo elétrico. Posteriormente o tecido cerebral foi processado (perfundido, crioprotegido, congelado e cortado) e então foram feitas as técnicas histoquímicas de: Nissl e Fluoro-Jade C (FJC, marcador de neurodegeneração). As respostas comportamentais foram analisadas mediante o uso do Índice de Gravidade para Crises Límbicas e da neuroetologia. Adicionalmente foi avaliada a expressão EEGráfica do 1º, 20º e 21º estímulos e verificada a presença/ausência de neurodegeneração em regiões do sistema límbico. Os resultados da análise comportamental obtidos nesse estudo foram comparados com os obtidos no protocolo de estimulação olfatória com TMT nas crises audiogênicas agudas de ratos da cepa WAR. O TMT desencadeou reações de medo e modificou as sequências comportamentais, reduziu a atividade motora e os comportamentos de autolimpeza. Dados qualitativos da cromatografia gasosa e algoritmos matemáticos possibilitaram estabelecer as concentrações na câmara para as diferentes doses de TMT. Além disso, a cromatografia gasosa identificou que 30 minutos é o tempo necessário para saturação e dessaturação da câmara ao TMT, e indicou uma saturação homogênea do interior dessa câmara. O TMT puro no abrasamento elétrico rápido em ratos Wistar foi capaz de reduzir significativamente o Índice de Gravidade para Crises Límbicas comparado à água, corroborando os dados neuroetológicos que indicam o efeito supressor do TMT nas crises, tanto para o modelo de abrasamento elétrico rápido quanto para as crises audiogênicas agudas. Os resultados da duração da pós-descarga EEGráfica primária no 21º estímulo foram inconclusivos, sendo necessárias outras análises empregando diferentes métodos analíticos. Com a técnica de FJC não foi possível verificar morte celular por necrose em qualquer região cerebral avaliada. / One of the most widely used experimental models to study temporal lobe epilepsy (TLE) is the kindling by electrical daily stimulation of the amygdala, the conventional kindling. A rapid and effective alternative to this model is rapid electrical kindling, also capable of generating limbic seizures, but with 10 electrical stimuli applied per day for 2 days. On the 3rd day an additional electrical stimulus is applied, the 21st stimulus, when antiepileptic drugs can be tested or mechanisms of plasticity and memory can be studied. Among the main areas activated in limbic seizures are the amygdaloid complex, the hippocampal formation, piriform cortex and adjacent neocortices. The involvement of the olfactory structures in TLE is old and studies indicate that exposure to an olfactory stimulus is capable to suppress or inhibit or induce the occurrence of seizures. All the clinical and experimental evidences provide scientific support for the hypothesis that the olfactory stimulation with 2,5-Dihydro-2,4,5-trimethylthiazoline (TMT), a powerful chemical substance derived from fox feces which biologically represents the \"predator smell can influence the seizures process evoked by electrical stimulation of the amygdala. The overall objective of this study was to evaluate the influence of olfactory stimulation with TMT in seizures of Wistar rats subjected to rapid electrical kindling of the amygdala. Therefore, the chemical parameters of TMT were evaluated, as well as behavioral responses of naive male Wistar rats exposed to the olfactory stimulus with different concentrations of TMT. Other group of rats was electrically stimulated in the amygdaloid complex, following the protocol of rapid electrical kindling and the electroencephalographic recordings (EEGraphic) obtained from the piriform cortex, hippocampal formation in addition to the amygdaloid complex. After scorched the animals were exposed to TMT or distilled water, prior to the 21st electrical stimulation. Subsequently the cerebral tissue was processed (perfused, cryoprotected, frozen and sliced) and then processed for Nissl and Fluoro-Jade C histochemistry (FJC, a marker of neurodegeneration). The behavioral responses were analyzed by using the Severity Index for Limbic Seizures and neuroethology. In addition to EEG, reviewed after the 1st, 20th and 21th stimuli we also examined the presence/absence of neurodegeneration in regions of the limbic system. The results obtained in this study were compared with those obtained in the protocol of olfactory stimulation with TMT on acute audiogenic seizures of rats from the WAR strain. The TMT triggered fear reactions and modified the behavioral sequences, reduced motor activity and grooming behavior. Qualitative data from gas chromatography and mathematical algorithms made possible to establish the concentrations in the camera for the different doses of TMT. In addition, the gas chromatography helped to identify that 30 minutes is the time required for saturation and desaturation of the camera to TMT and indicated a homogeneous saturation of the interior of such camera. The pure TMT in rapid electrical kindling in Wistar rats was able to significantly reduce the Severity Index for Limbic Seizures, compared to water, corroborating the data of the neuroethology method indicating the suppressive effect of TMT in seizures, in both, the model of rapid electrical kindling as well as the acute audiogenic seizures. However, the results of the duration of the EEGraphic primary after-discharge at the 21th stimulus were inconclusive, requiring further analysis using different analytical methods. With the technique of FJC it was not observed necrotic cell death in any studied brain region.

A Brain-Computer Interface (BCI) is a system to communicate with external world through the brain activity. The brain activity is measured by Electro-Encephalography (EEG) and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEGbased brain–computer interface (BCI). In this thesis BCI methods were applied on derived sources which by their EEG enhancement it became possible to obtain a more accurate EEG detection and brought a new application to BCI technology that are recognition of writing letters imagery from brain waves. The BCI system enables people to write and type letters by their brain activity (EEG). To this end, first part of the thesis is dedicated to EEG source reconstruction techniques to select the most optimal EEG channels for task classification purposes. Due to this reason the changes in EEG signal power from rest state to motor imagery task was used, to find the location of an active single equivalent dipole. Implementing an inverse problem solution on the power changes by Multiple Sparse Priors (MSP) method generated a scalp map where its fitting showed the localization of EEG electrodes. Having the optimized locations the secondary objective was to choose the most optimal EEG features and rhythm for an efficient classification. This became possible by feature ranking, 1- Nearest Neighbor leave-one-out. The feature vectors were computed by applying the combined methods of multitaper method, Pwelch. The features were classified by several methods of Normal densities based quadratic classifier (qdc), k-nearest neighbor classifier (knn), Mixture of Gaussians classification and Train neural network classifier using back-propagation. Results show that the selected features and classifiers are able to recognize the imagination of writing alphabet with the high accuracy. / BCI controls external devices and interacts with the environment by brain signals. Measured EEG signals over the motor cortex exhibit changes in power related to the movements or imaginations which are executed in motor tasks [1]. These changes declare increase or decrease of power in the alpha (8Hz-13Hz), and beta (13Hz-28Hz) frequency bands from resting state to motor imagery task that known as event related synchronization (in case of power increasing) and desynchronization (in case of power decreasing) [2]. The necessity to communicate with the external world for locked-in state (LIS) patients (a paralyzed patient who only communicates with eyes), made doctors and engineers motivated to develop a BCI technology for typing letters through brain commands. Many researches have been done around this area to ascertain the dream of typing for handicapped. In the brain some regions of the cerebral cortex (motor cortex) are involved in the planning, control, and execution of voluntary movements. Electroencephalography (EEG) signals are electrical potential generated by the nerve cells in the cerebral cortex. In order to execute motoric tasks, the EEG signals are appeared over the motor cortex [1]. The measured brain response to a stimulus is called eventrelated potential (ERP). P300-event related potential (ERP) is an evoked neuron response to an external auditory or visual stimulus that is detectable in scalp-recorded EEG (The P300 is evoked potential which occurs across the parieto-central on the skull 300 ms after applying the stimulus). Farwell and Donchin have proven in a P300-based BCI speller [3] that P300 response is a reliable signal for controlling a BCI system. They described the P300 speller, in which alphanumeric characters are represented in a matrix grid of six-by-six matrix. The user should focus on one of the 36 character cells while each row and column of the grid is intensified randomly and sequentially. The P300, observed in EEG signals, is created by the intersection of the target row and column which causes detection of the target stimuli with a probability of 1/6 (in case of high accuracy of flashing operation). Also when the target stimulus is rarely presented in the random sequence of stimuli causes a neural reaction to unpredictable but recognizable event and a P300 response is evoked [3]. Generally when the subject is involved with the task to recognize the targets, the P300 wave happens and the signal amplitude varies with the unlikelihood of the targets. Its dormancy changes with the difficulty of recognizing the target stimulus from the standard stimuli [3].The attended character of the matrix can be extracted by proper feature extraction and classification of P300. A plenty of procedures for feature extraction and classification have been applied to improve the performance of originally reported speller [3], such as stepwise linear discriminate analysis (SWLDA) [4, 5], wavelets [1], support vector machines [6, 7, 8] and matched filtering [9]. Till now, BCI-related P300 research has mostly considered on signals from standard P300 scalp locations. While in [10, 11, 12, 13, 14, 15, 16] it has been proven that the use of additional locations, especially posterior sites, may improve classification accuracy, but it has not been addressed to particular offline and online studies. Recently, auditory version improvement of the visual P300 speller allows locked in patients who have problem in the visual system to use the P300 speller system by relating two numbers to each letter which indicate the row and column of letter position [17]. Now a new technology is needed which can substitute a keyboard with no alphabet menu. The technology will be handy for blind people and useful for healthy persons who need to work hands free with their computer or mobile. The aim of this thesis is to improve EEG detection through source localization for a new BCI application to type with EEG signals without using alphabet menu. / +98-9359576229

Contexte théorique : L’encéphalopathie hypoxique ischémique (EHI) est une condition du nouveau-né dans laquelle les mécanismes des variables métaboliques ne sont pas totalement compris. Cette population est particulièrement à risque d’hypo- ou d’hyperglycémie néonatales (HHN). Devant le manque de données sur le fonctionnement métabolique à la suite d’une EHI, cette étude vise à déterminer l’association entre une HHN et l’activité cérébrale mesurée par électroencéphalographie (EEG). Méthodologie : 49 participants avec EHI ont été recrutés au CHU Sainte-Justine peu après leur naissance. Ils ont été monitorés en continu à l’aide de l’EEG et des segments d’intérêt se retrouvant dans les 48 premières heures de vie ont été analysés. L’anormalité de l’activité cérébrale est déterminée selon une analyse quantitative du niveau de discontinuité caractérisée par une proportion de faibles amplitudes (seuils de 25, 15, 12,5, 10 et 7,5 uV) dans le tracé EEG. Les données de glycémie ont été recueillies de façon intermittente par le biais de prises de sang et de glucomètres de chevet. Les participants ont été répartis en 4 groupes : normoglycémie, hyperglycémie, hypoglycémie et glycémie variable (hypo- et hyper-). Résultats : L’analyse de covariation non -paramétrique a relevé une différence significative entre les ratios de discontinuité pour le seuil de 15 uV (F = 3,070 p = 0,037). Les analyses de comparaisons appariées ont montré une différence positive entre le groupe VARIABLE et le groupe contrôle (NORMO-) pour tous les seuils ainsi qu’une différence positive entre le groupe HYPER- et le groupe contrôle pour 4 des 5 seuils (25, 15, 12,5 et 7,5 uV). Aucune différence n’a été relevé entre le groupe HYPO- et le groupe contrôle pour tous les seuils. Conclusions : La variabilité glycémique et l’hyperglycémie seule ont été montrées comme étant associées à une activité cérébrale altérée caractérisée par un tracé de plus faible amplitude mesurée avec l’EEG. / Background: Hypoxic ischemic encephalopathy (HIE) is a newborn condition in which the underlying mechanisms still require further understanding. This clinical population is particularly prone to neonatal hypo- and hyperglycemia (NHH). Given the need to improve our understanding of metabolic functioning following HIE, this study aims to determine the association of NHH on the brain’s background electrophysiological activity measured by electroencephalography (EEG). Methodology: Forty-nine newborns with HIE were recruited at Sainte-Justine University Hospital Center. Continuous EEG monitoring was started as soon as possible and segments of interest in the first 48h of life were analyzed. Brain activity was quantitatively assessed according to an index of discontinuity characterized by the proportion of low EEG amplitudes per segment (< 25, 15, 12.5, 10 and 7.5 uV cutoffs). Glucose measurements were intermittently collected using blood samples and bedside glucometers and were retrospectively retrieved from medical charts. Participants were separated in 4 groups : normoglycemia, hyperglycemia, hypoglycemia and both (hyper- and hypo-). Results: The non-parametric covariance analyses revealed a significant difference between the discontinuity index for the 15 uV threshold (F = 3.070 p = 0.037). The pairwise comparisons showed a positive difference between the group BOTH and the control group (NORMO-) for every thresholds, the labile glucose group having a higher discontinuity index. A similar difference was found between the HYPERGLYCEMIA group and the control group for 4 out 5 thresholds (25, 15, 12.5 and 7.5 uV). No difference was found between the HYPOGLYCEMIA group and the control group. Conclusion: An abnormal glycemic profile, particularly glucose lability and hyperglycemia alone, were shown to be associated with abnormal brain activity characterized by a higher discontinuity index on the EEG.

No description available.

<p>The goal of this study was to elucidate whether articulations of visual-speech are processed phonologically, and in the same manner as auditory-speech. Phonological processing, measured through the amplitude of the Phonological Mapping Negativity (PMN), was compared across three conditions using the electroencephalogram (EEG). Planned polynomial contrasts compared conditions of related and unrelated linguistic stimuli versus a non-linguistic control stimulus. A significant Site x Condition polynomial trend at posterior sites (Pz and Oz) during the N400 tine window revealed that the unrelated condition was most negative in amplitude, an N400-like deflection in the control condition reached similar negative amplitude, while the related condition was the most positive. A significant quadratic trend of PMN amplitude differentiated between the linguistic conditions and the non-linguistic control at site Fz, but did not differentiate the related and unrelated linguistic conditions from each other. These results support a conclusion that non-lexical speech-like and gurning motions of the lips are treated differently than articulations of a meaningful nature. Moreover, the PMN response patterned similarly in the linguistic conditions, compared to the non-linguistic control, indicating phonological processing. The prediction that PMN amplitude will distinguish visual-speech events congruent or incongruent to a phonologically constrained context was not supported.</p> / Master of Science (MSc)

L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interaction (hyperscanning) permet d'étendre notre compréhension des neurosciences sociales. Nous proposons un cadre pour l'hyperscanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Les travaux de cette thèse comportent trois contributions interdépendantes. Notre première contribution est le développement d'une plateforme expérimentale sous la forme d'un jeu vidéo multijoueur, nommé Brain Invaders 2, contrôlé par la classification de potentiels évoqués visuels enregistrés par électroencéphalographie (EEG). Cette plateforme est validée par deux protocoles expérimentaux comprenant dix-neuf et vingt-deux paires de sujets et utilise différentes approches de classification adaptative par géométrie riemannienne. Ces approches sont théoriquement et expérimentalement comparées et nous montrons la supériorité de la fusion des classifieurs indépendants sur la classification d'un hypercerveau durant la seconde contribution. L'analyse de coïncidence des signaux entre les individus est une approche classique pour l'hyperscanning, elle est pourtant difficile quand les signaux EEG concernés sont transitoires avec une grande variabilité (intra- et inter-sujet) spatio-temporelle et avec un faible rapport signal-à-bruit. En troisième contribution, nous proposons un nouveau modèle composite de séparation aveugle de sources physiologiquement plausibles permettant de compenser cette variabilité. Une solution par diagonalisation conjointe approchée est proposée avec une implémentation d'un algorithme de type Jacobi. A partir des données de Brain Invaders 2, nous montrons que cette solution permet d'extraire simultanément des sources d'artéfacts, des sources d'EEG évoquées et des sources d'EEG continues avec plus de robustesse et de précision que les modèles existants. / The study of several brains interacting (hyperscanning) with neuroimagery allows to extend our understanding of social neurosciences. We propose a framework for hyperscanning using multi-user Brain-Computer Interfaces (BCI) that includes several social paradigms such as cooperation or competition. This dissertation includes three interdependent contribution. The first contribution is the development of an experimental platform consisting of a multi-player video game, namely Brain Invaders 2, controlled by classification of visual event related potentials (ERP) recorded by electroencephalography (EEG). The plateform is validated through two experimental protocols including nineteen and twenty two pairs of subjects while using different adaptive classification approaches using Riemannian geometry. Those approaches are theoretically and experimentally compared during the second contribution ; we demonstrates the superiority in term of accuracy of merging independent classifications over the classification of the hyperbrain during the second contribution. Analysis of inter-brain synchronizations is a common approach for hyperscanning, however it is challenging for transient EEG waves with an great spatio-temporal variability (intra- and inter-subject) and with low signal-to-noise ratio such as ERP. Therefore, as third contribution, we propose a new blind source separation model, namely composite model, to extract simultaneously evoked EEG sources and ongoing EEG sources that allows to compensate this variability. A solution using approximate joint diagonalization is given and implemented with a fast Jacobi-like algorithm. We demonstrate on Brain Invaders 2 data that our solution extracts simultaneously evoked and ongoing EEG sources and performs better in term of accuracy and robustness compared to the existing models.

<p>A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. </p><p>Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network. </p><p>A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.</p>

A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network. A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.

A recent model of Parkinson’s disease (PD) suggests that the neuropathological, behavioural and cognitive symptoms progress in stages. There is substantial evidence for a prodromal stage of PD, during which time pre-motor symptoms develop. Rapid eye movement (REM) sleep behaviour disorder (RBD) is a risk factor for developing PD and may be part of the pre-motor stage. In both disorders, neuropathological α-synuclein aggregates are thought to be a direct cause of the resulting symptoms. One model has shown that in rats, proteasome inhibition produced by systemic exposure to environmental toxins results in α-synuclein pathology and motor behaviour dysfunction that mimics the progression of PD in humans. The present study examined the hypothesis that the systemic proteasome inhibition model would produce pre-Parkinsonian RBD-like pathology in rats. It was expected that sleep disturbances would be seen prior to behavioural disturbances in rats treated systemically with PSI (a proteasome inhibitor). Following baseline sleep recording and training on the inclined beam-traverse task, rats were injected with PSI (a proteasome inhibitor) or ethanol (control), 6 times over 2 wk. Sleep recording over 8 wk and behavioural testing over 16 wk provided no evidence of sleep disturbances or motor dysfunction. Post-mortem immunohistochemical analyses of brain tissue provided no evidence of PSI-associated α-synuclein aggregates in the locus coeruleus, subcoeruleus (dorsal part), or substantia nigra (areas involved in RBD and/or PD). These results did not provide support for RBD as a prodromal phase of PD within the systemic proteasome inhibitor-based model and add to a growing body of research reporting inconsistent findings using this model. We suggest that systemic PSI exposure in rats does not produce a viable model of RBD or PD. Whether RBD is an early symptom in the progression of PD remains to be established. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2010-04-28 12:04:50.613

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.